Inferring the time-varying coupling of dynamical systems with temporal convolutional autoencoders

• URL: http://arxiv.org/abs/2406.03212v1
• Date: Wed, 5 Jun 2024 12:51:20 GMT
• Title: Inferring the time-varying coupling of dynamical systems with temporal convolutional autoencoders
• Authors: Josuan Calderon, Gordon J. Berman,
• Abstract summary: We introduce Temporal Autoencoders for Causal Inference (TACI) TACI combines a new surrogate data metric for assessing causal interactions with a novel two-headed machine learning architecture. We demonstrate TACI's ability to accurately quantify dynamic causal interactions across a variety of systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Most approaches for assessing causality in complex dynamical systems fail when the interactions between variables are inherently non-linear and non-stationary. Here we introduce Temporal Autoencoders for Causal Inference (TACI), a methodology that combines a new surrogate data metric for assessing causal interactions with a novel two-headed machine learning architecture to identify and measure the direction and strength of time-varying causal interactions. Through tests on both synthetic and real-world datasets, we demonstrate TACI's ability to accurately quantify dynamic causal interactions across a variety of systems. Our findings display the method's effectiveness compared to existing approaches and also highlight our approach's potential to build a deeper understanding of the mechanisms that underlie time-varying interactions in physical and biological systems.

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